Opensky

OpenSky provides api to get data for flights in progress. It is rate limited

Code

import requests
import pandas as pd
import geopandas

Code

def fetch_opensky_data():
    url = "https://opensky-network.org/api/states/all"
    response = requests.get(url)
    data = response.json()

    columns = [
        "icao24", "callsign", "origin_country", "time_position", "last_contact",
        "longitude", "latitude", "baro_altitude", "on_ground", "velocity",
        "heading", "vertical_rate", "sensors", "geo_altitude", "squawk",
        "spi", "position_source"
    ]

    df_raw = pd.DataFrame(data['states'], columns=columns)
    return (
        df_raw
        .assign(
            time_position=pd.to_datetime(df_raw["time_position"], unit="s"),
            last_contact=pd.to_datetime(df_raw["last_contact"], unit="s"),
        )
    )

df_flights = fetch_opensky_data()


df_flihts_non_nan = df_flights.dropna(subset=["longitude", "latitude"])
gdf = geopandas.GeoDataFrame(
    df_flihts_non_nan,
    geometry=geopandas.points_from_xy(df_flihts_non_nan.longitude, df_flihts_non_nan.latitude),
    crs="EPSG:4326",
)

gdf.explore(fullscreen=True, tooltip=False, popup=True)

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Flights in world at notebook time

Number of flights by origin_country

Code

(
    df_flights
    .groupby("origin_country")
    .agg(num_flights=("icao24", "count"))
    .sort_values("num_flights", ascending=False)
    .head(20)
)

	num_flights
origin_country
United States	5932
Canada	436
United Kingdom	361
Australia	260
Ireland	230
Brazil	124
Turkey	124
Germany	107
Spain	106
China	98
Malta	94
United Arab Emirates	83
Austria	80
India	71
Kingdom of the Netherlands	70
France	67
Mexico	66
Republic of Korea	66
New Zealand	64
Japan	63

Top 20 countries with most flights

All flights

For reference

Code

df_flights

	icao24	callsign	origin_country	time_position	last_contact	longitude	latitude	baro_altitude	on_ground	velocity	heading	vertical_rate	sensors	geo_altitude	squawk	spi	position_source
0	e8027c	LPE2338	Chile	2025-05-28 21:48:06	2025-05-28 21:48:06	-79.1481	-8.7767	9136.38	False	219.94	333.32	0.00	None	9784.08	None	False	0
1	a3feec	N357BE	United States	2025-05-28 21:48:06	2025-05-28 21:48:06	-97.3697	41.8808	12496.80	False	213.29	161.00	0.33	None	12710.16	2455	False	0
2	39de4d	TVF31HD	France	2025-05-28 21:45:25	2025-05-28 21:45:25	-5.8982	37.4228	NaN	True	2.57	317.81	NaN	None	NaN	7621	False	0
3	a5953a	TRF559	United States	2025-05-28 21:47:17	2025-05-28 21:47:20	-96.2515	33.0461	205.74	False	30.97	138.37	-2.93	None	213.36	1200	False	0
4	a5f84e	N484JS	United States	2025-05-28 21:46:04	2025-05-28 21:46:04	-98.6936	29.7275	NaN	True	0.39	92.81	NaN	None	NaN	None	False	0
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
9356	a4359b	UPS5804	United States	2025-05-28 21:48:06	2025-05-28 21:48:06	-100.8087	41.3746	10370.82	False	192.56	267.70	0.00	None	10607.04	3454	False	0
9357	abc0e4	SWA2446	United States	2025-05-28 21:48:06	2025-05-28 21:48:06	-90.2898	38.5997	1097.28	False	138.81	107.02	-2.28	None	1127.76	3631	False	0
9358	4b1901	EDW10	Switzerland	2025-05-28 21:48:06	2025-05-28 21:48:06	-115.5372	54.1183	11582.40	False	223.59	220.80	0.00	None	12009.12	None	False	0
9359	4caa32	ITY629	Ireland	2025-05-28 21:48:06	2025-05-28 21:48:06	-79.5480	43.5888	11277.60	False	258.09	65.63	0.00	None	11551.92	5771	False	0
9360	c00734	WJA1611	Canada	2025-05-28 21:48:05	2025-05-28 21:48:05	-105.2619	48.9154	11590.02	False	248.25	282.57	0.00	None	11910.06	None	False	0

9361 rows × 17 columns